Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model

By Duygu Balcan, Bruno Gonçalves, Hao Hu, José J. Ramasco, Vittoria Colizza, Alessandro Vespignani. Journal of Computer Science, 2010

507 citations recorded by the source corpus.

Here we present the Global Epidemic and Mobility (GLEaM) model that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. We discuss the flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies. This makes GLEaM suitable for the computational modeling and anticipation of the spatio-temporal patterns of global epidemic spreading, the understanding of historical epidemics, the assessment of the role of human mobility in shaping global epidemics, and the analysis of mitigation and containment scenarios.

Subfields: Epidemiology, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/0dcbc585a78154fc523e74301cd983f0411da1a2

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Citations in the corpus

Geo-located Twitter as proxy for global mobility patterns (2013, background): “xploring migration flows (Castles and Mill 1998; Greenwood 1985; Sassen 1999), touristic activity (Miguéns and Mendes 2008) but also the spread of diseases and epidemic modeling (Bajardi et al. 2011; Balcan et al. 2010). Traditionally, those studies relied either on the aggregated and temporally-spa”
A Survey on Deep Learning for Human Mobility (2020, background): “Flow generation is crucial to many aspects of our society, such as transport planning [54] and spatial economics [97, 142, 148], to reduce inequalities and to design more sustainable communities, and the modeling of epidemic spreading patterns [9, 31, 114, 231].”
Inter-Series Attention Model for COVID-19 Forecasting (2020, background): “Moreover, geographic information can also be incorporated into the mechanistic models to better illustrate the spreading process of an infectious disease [3, 4].”
Human mobility: Models and applications (2017, methodology): “The mobility is then simulated at two levels: long range air transport, which is calibrated from the world airport network, and short-range mobility obtained from either census commuting data or estimated using the gravity law [165].”
A Deep Gravity model for mobility flows generation (2020, methodology): “Given its ability to generate spatial ﬂ ows and traf ﬁ c demand between locations, the gravity model has been used in various contexts such as transport planning 48 , spatial economics 18,49,50 , and the modeling of epidemic spreading patterns 51 – 54 .”
Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, background): “[148] Duygu Balcan, Bruno Gonçalves, Hao Hu, José J.”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “[102] † combine related internet search and news media activity with data from the Chinese Center for Disease Control and daily forecasts from GLEAM [103] , an agent-based mechanistic model, in order to produce 2-day forecasts for a range of daily statistics.”